code: def __init__(self, input_size, output_size, activation=None):
      super(GatedDense, self).__init__()
  
      self.activation = activation
      self.sigmoid = nn.Sigmoid()
      self.h = nn.Linear(input_size, output_size)
      self.g = nn.Linear(input_size, output_size)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import VAMP, AutoModel, VAMPConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new GatedDense object from inherited base classes: nn with example: obj = GatedDense(input_size, output_size, activation)